Speaker "Batool Haider" Details

Abstract :

For UnitedHealth Group (UHG) supports 183,000 employees working in 22 countries, human capital is both a valuable resource and a challenge. UHG has launched several initiatives to analyze enterprise-wide human capital potential using data analytics. Predicting retirement events is one of them. Like many other major corporates, UHG can be divided into two major segments- “Services” and “Benefits”. First being the technology driven segment where dynamics of the market and talent landscape change fervently and managers are constantly looking for new talent equipped with knowledge of the latest market trends and tools. This segment thus prefers early retirement among its aged employees. The second segment executes more traditional, yet key strategic operations. Here experience and deep understanding of UHG business is hugely valued, and would therefore prefer late retirement events so as to retain most of its experienced human capital. The goal of this study is to be able to identify the key factors that lead an employee to seek retirement and be able to predict retirement trends for the upcoming years. This will help both the segments formulate effective policies that favor the employees and UHG business, altogether. 6 years data of thousands of employees were analyzed. At the talk, some important findings of the study will be shared, alongside challenges and direction of future work in this area of workforce analytics.

Profile :

Batool is currently working as a Data Scientist for UnitedHealth Group’s Work Force Analytics Research team. Her team is engaged in helping UHG understand its massive talent landscape, discover new patterns and uncover previously hidden knowledge.
Before joining UHG, Batool served as a Research Assistant for two years at SUPRI-B- the Reservoir Simulation Research team at the Energy Resources Engineering department at Stanford University. Prior to that, she worked for Weatherford International Limited where she was awarded with the ‘Core Behavior Award on Innovation’. She has presented her research studies in many international conferences, and has conducted several Machine Learning projects with research teams at Stanford and Halliburton. These range from predicting cardiac arrhythmia in patients, to ranking the important precursors of high producing gas wells. She has also served as the Lead Tutor Computation and Natural Sciences at Stanford’s Summer Session, and the instructor of a popular workshop series at Stanford titled ‘Machine Learning in Plain English’.
Batool holds Master’s of Science degree in Petroleum Engineering from Stanford University.